your Docker ® environment must support NVIDIA GPUs. To run a container, issue the appropriate command as explained in Running A Container and specify the registry, repository, and tags. About this task runtime resources of the container by including additional flags and settings that are used with the command. These flags and settings are described in Running A Container. ‣ The GPUs are explicitly defined NVIDIA Virtual GPUs (vGPUs). Procedure 1. Issue the command for the applicable release of the container that you want. The following command assumes that you want to pull the latest container. docker

Keras. Prerequisites You must satisfy the following requirements:  Any kind of OS (Windows, Linux or Mac)  Python version 3.5 or higher. Python Keras is python based neural network library Linux/Mac OS Linux or mac OS users, go to your project root directory and type the below command to create virtual environment, python3 -m venv kerasenv After executing the above command, “kerasenv” Windows user can use the below command, py -m venv keras Step 2: Activate the environment This step will configure python and pip executables in your shell path. Linux/Mac OS Now we have created

the Image package as well (available for Windows as an option in the installer, and available for Linux from Octave-Forge ). 2 Linear Regression Recall that the linear regression model is hθ(x) = θT descent, we need to add the x0 = 1 intercept term to every example. To do this in Matlab/Octave, the command is m = length (y ) ; % st or e the number of t r a i n i n g examples x = [ ones (m, 1) , x ] ; iterations). After convergence, record the final values of θ0 and θ1 that you get, and plot the straight line fit from your algorithm on the same graph as your training data according to θ. The plotting commands

GGUF（由 GPT 生成的统一格式）模型。欲了解更多详情，请参阅官方 GitHub 仓库。以下我们将演示如何 使用 llama.cpp 运行 Qwen。 1.4.1 准备 这个示例适用于 Linux 或 MacOS 系统。第一步操作是：“克隆仓库并进入该目录： git clone https://github.com/ggerganov/llama.cpp cd llama.cpp 然后运行 大规模语言模型。Qwen1.5 已经正式成为 LM Studio 的一部分。祝你使用愉快！ 1.5 Ollama Ollama 帮助您通过少量命令即可在本地运行 LLM。它适用于 MacOS、Linux 和 Windows 操作系统。现在， Qwen1.5 正式上线 Ollama，您只需一条命令即可运行它： ollama run qwen 接着，我们介绍在 Ollama 使用 Qwen 模型的更多用法 text-generation-webui 你可以根据你的操作系统直接运行相应的脚本，例如在 Linux 系统上运行 start_linux.sh ，在 Windows 系统上运行 start_windows.bat ，在 MacOS 系统上运行 start_macos.sh ，或者在 Windows 子系统 Linux（WSL）上运行 start_wsl.bat 。另外，你也可以选择手动在 conda 环境中安装所需的依赖项。这

environment, you should be able to run Python scripts that uses PyTorch/TensorFlow by the python command: Installing Your Own Miniconda 1. Download Miniconda installer. 2. Run the installer. The argument python python_script.py wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh sh Miniconda3-latest-Linux-x86_64.sh -b \ -p /rwproject/kdd-db/`whoami`/miniconda3 /rwproject/kdd-db/ Miniconda is successfully installed, you should be able to see the usage of conda using the following command: Installing Your Own PyTorch You can install PyTorch to the default environment (i.e., the base

Miniconda3-py39_4.12.0-MacOSX-x86_64.sh -b 如果我们使用Linux，假设Python版本是3.9（我们的测试版本），将下载名称包含字符串“Linux”的bash脚 本，并执行以下操作： # 文件名可能会更改 sh Miniconda3-py39_4.12.0-Linux-x86_64.sh -b 接下来，初始化终端Shell，以便我们可以直接运行conda。 一只猫、一只公鸡、一只狗、一头驴 学习预测不相互排斥的类别的问题称为多标签分类（multi‐label classification）。举个例子，人们在技术博客 上贴的标签，比如“机器学习”“技术”“小工具”“编程语言”“Linux”“云计算”“AWS”。一篇典型的文章可 能会用5～10个标签，因为这些概念是相互关联的。关于“云计算”的帖子可能会提到“AWS”，而关于“机 器学习”的帖子也可能涉及“编程语言”。 此外， 3，其中系数2是切线的斜率。 x = np.arange(0, 3, 0.1) plot(x, [f(x), 2 * x - 3], 'x', 'f(x)', legend=['f(x)', 'Tangent line (x=1)']) 2.4. 微积分 67 2.4.2 偏导数 到目前为止，我们只讨论了仅含一个变量的函数的微分。在深度学习中，函数通常依赖于许多变量。因此，我 们需要将微分的思想推广到多元函数（multivariate

classes. In Matlab/Octave, you can separate the positive class and the negative class using the find command: % find returns the i n d i c e s of the % rows meeting the s p e c i f i e d condition pos = boundary is defined as the line where P(y = 1|x; θ) = g(θT x) = 0.5 which corresponds to θT x = 0 Plotting the decision boundary is equivalent to plotting the θT x = 0 line. When you are finished, your

too much? The original (pre-quantization) image is shown in figure 2-6. Get the image using this command: !wget https://github.com/reddragon/book-codelabs/raw/main/pia23378-16.jpeg Solution: First, we certain trade-offs. We hope that this chapter helps more deep learning models to cross the finish line. The next chapter will introduce learning techniques to improve quality metrics like accuracy and

0.0)) # 绘制权值矩阵范围 surf = ax.plot_surface(X, Y, weights, cmap=plt.get_cmap('rainbow'), line width=0) # 设置坐标轴名 ax.set_xlabel('网格 x 坐标', fontsize=16, rotation = 0) ax.set_ylabel('网格 图 14.4 行走机器人 目前在 Windows 平台安装 Gym 环境可能会遇到一些问题，因为 Gym 调用的部份软件 库对 Windows 平台支持并不友好，推荐大家使用基于 Linux 的图形系统安装。本章使用的 平衡杆游戏环境可以在 Windows 平台上完美使用，但是其它复杂的游戏环境则不一定。 运行 pip install gym 命令只会安装 Gym 环境的基本库，平衡杆游戏已经包含在基本库 learning,” Machine Learning, 卷 8, pp. 229-256, 01 5 1992. [5] G. A. Rummery 和 M. Niranjan, “On-Line Q-Learning Using Connectionist Systems,” 1994. [6] H. Hasselt, A. Guez 和 D. Silver, “Deep Reinforcement

Expectation-Maximization (EM) algorithm. 6.1 Convex Sets and Convex Functions A set C is convex if the line segment between any two points in C lies in C, i.e., for ∀x1, x2 ∈ C and ∀θ with 0 ≤ θ ≤ 1, we have we have the inequality in the fifth line. The sixth equality also comes from Eq. (31) 13 To tighten the lower bound, we should let the equality (in the forth line) hold. According to Jensen’s inequality in particular holds for Qi = Q[t] i , according to Eq. (32). We have the inequality in the second line, because θ[t+1] is calculated by θ[t+1] = arg max θ � i � z(i)∈Ω Q[t] i (z(i)) log p(x(i), z(i);